Managing user access to query results

ABSTRACT

A system or computer usable program product for redacting QA system answer information based on user access to content including analyzing a corpus by natural language processing techniques, wherein the corpora includes non-sensitive and sensitive content, and storing the analyzed corpora in memory; receiving a user question to be answered by utilizing the analyzed corpora; utilizing a processor to determine a set of answer information by processing using the corpora; determining a user access right to sensitive content; and redacting an answer information item from the set of answer information if sensitive content to which the user does not have access was used to determine the answer information item.

BACKGROUND

1. Technical Field

The present invention relates generally to managing user access to queryresults, and in particular, to a computer implemented method forselectively redacting query results based on data source restrictionsand user privileges.

2. Description of Related Art

With the use and improvement of natural language processing (NLP),computers are becoming better at processing human originated speech andwritings. Human language is very complex and includes a variety ofambiguities and uncertainties that are difficult for computers tomanage. Hand written rules have been developed to assist in thisprocess, but were insufficient to solve the complexity of problemsencountered in analyzing human language. Subsequently, statisticalmodels were developed and more recently statistical machine learningtechniques have been utilized to improve NPL substantially.

NLP has been utilized in a variety of applications including spellingand grammar correction in documents being typed, summarizing text ordocuments, language translation, and for providing answers to userqueries. In many of these applications, NLP is utilized in multiplecapacities. For example, in providing answers to use queries, a computermay have utilized NLP for analyzing a multitude of documents (corpora)in electronic form to develop a content database with indices to theunderlying documents. The corpora can include human generated documents,machine translated documents, web pages, etc. that were produced orcaptured in electronic form. This content database can then be utilizedto rapidly search and access the underlying corpora, thereby quicklyproviding an answer to a user in response to a user query that is alsoanalyzed with NLP.

SUMMARY

The illustrative embodiments provide a method, system, and computerusable program product for redacting QA system answer information basedon user access to content including analyzing a corpus by naturallanguage processing techniques, wherein the corpora includesnon-sensitive and sensitive content, and storing the analyzed corpora inmemory; receiving a user question to be answered by utilizing theanalyzed corpora; utilizing a processor to determine a set of answerinformation by processing using the corpora; determining a user accessright to sensitive content; and redacting an answer information itemfrom the set of answer information if sensitive content to which theuser does not have access was used to determine the answer informationitem.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, further objectivesand advantages thereof, as well as a preferred mode of use, will best beunderstood by reference to the following detailed description ofillustrative embodiments when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a block diagram of an illustrative data processing system inwhich various embodiments of the present disclosure may be implemented;

FIG. 2 is a block diagram of an illustrative network of data processingsystems in which various embodiments of the present disclosure may beimplemented;

FIG. 3 is a block diagram of a question and answer (QA) system in whichvarious embodiments may be implemented;

FIG. 4A to 4F are pictorial diagrams of a user interface in whichvarious embodiments may be implemented;

FIG. 5 is a flow diagram of generating and redacting answers inaccordance with a first embodiment;

FIG. 6 is a flow diagram of generating and redacting answers inaccordance with a second embodiment;

FIGS. 7A and 7B are a flow diagrams of generating a set of answers forpossible redaction in which various embodiments may be implemented; and

FIGS. 8A to 8D are block diagrams of types of database records in whichvarious embodiments may be implemented.

DETAILED DESCRIPTION

Processes and devices may be implemented and utilized for managing useraccess to query results. These processes and apparatuses may beimplemented and utilized as will be explained with reference to thevarious embodiments below.

FIG. 1 is a block diagram of an illustrative data processing system inwhich various embodiments of the present disclosure may be implemented.Data processing system 100 is one example of a suitable data processingsystem and is not intended to suggest any limitation as to the scope ofuse or functionality of the embodiments described herein. Regardless,data processing system 100 is capable of being implemented and/orperforming any of the functionality set forth herein such as managinguser access to query results.

In data processing system 100 there is a computer system/server 112,which is operational with numerous other general purpose or specialpurpose computing system environments, peripherals, or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with computer system/server112 include, but are not limited to, personal computer systems, servercomputer systems, thin clients, thick clients, hand-held or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system/server 112 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 112 may be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 112 in data processing system100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 112 may include, but are notlimited to, one or more processors or processing units 116, a systemmemory 128, and a bus 118 that couples various system componentsincluding system memory 128 to processor 116.

Bus 118 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 112 typically includes a variety ofnon-transitory computer system readable media. Such media may be anyavailable media that is accessible by computer system/server 112, and itincludes both volatile and non-volatile media, removable andnon-removable media.

System memory 128 can include non-transitory computer system readablemedia in the form of volatile memory, such as random access memory (RAM)130 and/or cache memory 132. Computer system/server 112 may furtherinclude other non-transitory removable/non-removable,volatile/non-volatile computer system storage media. By way of example,storage system 134 can be provided for reading from and writing to anon-removable, non-volatile magnetic media (not shown and typicallycalled a “hard drive”). Although not shown, a USB interface for readingfrom and writing to a removable, non-volatile magnetic chip (e.g., a“flash drive”), and an optical disk drive for reading from or writing toa removable, non-volatile optical disk such as a CD-ROM, DVD-ROM orother optical media can be provided. In such instances, each can beconnected to bus 118 by one or more data media interfaces. Memory 128may include at least one program product having a set (e.g., at leastone) of program modules that are configured to carry out the functionsof the embodiments. Memory 128 may also include data that will beprocessed by a program product.

Program/utility 140, having a set (at least one) of program modules 142,may be stored in memory 128 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 142 generally carry out the functionsand/or methodologies of the embodiments. For example, a program modulemay be software for managing user access to query results.

Computer system/server 112 may also communicate with one or moreexternal devices 114 such as a keyboard, a pointing device, a display124, etc.; one or more devices that enable a user to interact withcomputer system/server 112; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 112 to communicate withone or more other computing devices. Such communication can occur viaI/O interfaces 122 through wired connections or wireless connections.Still yet, computer system/server 112 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter120. As depicted, network adapter 120 communicates with the othercomponents of computer system/server 112 via bus 118. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system/server 112.Examples, include, but are not limited to: microcode, device drivers,tape drives, RAID systems, redundant processing units, data archivalstorage systems, external disk drive arrays, etc.

FIG. 2 is a block diagram of an illustrative network of data processingsystems in which various embodiments of the present disclosure may beimplemented. Data processing environment 200 is a network of dataprocessing systems such as described above with reference to FIG. 1.Software applications such as for managing user access to query resultsmay execute on any computer or other type of data processing system indata processing environment 200. Data processing environment 200includes network 210. Network 210 is the medium used to provide simplex,half duplex and/or full duplex communications links between variousdevices and computers connected together within data processingenvironment 200. Network 210 may include connections such as wire,wireless communication links, or fiber optic cables.

Server 220 and client 240 are coupled to network 210 along with storageunit 230. In addition, laptop 250 and facility 280 (such as a home orbusiness) are coupled to network 210 including wirelessly such asthrough a network router 253. A mobile phone 260 may be coupled tonetwork 210 through a mobile phone tower 262. Data processing systems,such as server 220, client 240, laptop 250, mobile phone 260 andfacility 280 contain data and have software applications includingsoftware tools executing thereon. Other types of data processing systemssuch as personal digital assistants (PDAs), smartphones, tablets andnetbooks may be coupled to network 210.

Server 220 may include software application 224 and data 226 formanaging user access to query results or other software applications anddata in accordance with embodiments described herein. Storage 230 maycontain software application 234 and a content source such as data 236for managing user access to query results. Other software and contentmay be stored on storage 230 for sharing among various computer or otherdata processing devices. Client 240 may include software application 244and data 246. Laptop 250 and mobile phone 260 may also include softwareapplications 254 and 264 and data 256 and 266. Facility 280 may includesoftware applications 284 and data 286. Other types of data processingsystems coupled to network 210 may also include software applications.Software applications could include a web browser, email, or othersoftware application for managing user access to query results.

Server 220, storage unit 230, client 240, laptop 250, mobile phone 260,and facility 280 and other data processing devices may couple to network210 using wired connections, wireless communication protocols, or othersuitable data connectivity. Client 240 may be, for example, a personalcomputer or a network computer.

In the depicted example, server 220 may provide data, such as bootfiles, operating system images, and applications to client 240 andlaptop 250. Server 220 may be a single computer system or a set ofmultiple computer systems working together to provide services in aclient server environment. Client 240 and laptop 250 may be clients toserver 220 in this example. Client 240, laptop 250, mobile phone 260 andfacility 280 or some combination thereof, may include their own data,boot files, operating system images, and applications. Data processingenvironment 200 may include additional servers, clients, and otherdevices that are not shown.

In the depicted example, data processing environment 200 may be theInternet. Network 210 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 200 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 2 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 200 may be used forimplementing a client server environment in which the embodiments may beimplemented. A client server environment enables software applicationsand data to be distributed across a network such that an applicationfunctions by using the interactivity between a client data processingsystem and a server data processing system. Data processing environment200 may also employ a service oriented architecture where interoperablesoftware components distributed across a network may be packagedtogether as coherent business applications.

FIG. 3 is a block diagram of a question and answer (QA) system in whichvarious embodiments may be implemented. This system is an example forillustrative purposes. Many alternative embodiments could be implementedwith similar functionality by one of ordinary skill in the art.

A user 300 utilizes a user system 305 to communicate with QA system 320across a network 310. The user may be within a company or other smallentity communicating with a locally implemented QA system across aninternal network system. The user may also be any person worldwidecommunicating with a QA system located on a remote server or in thecloud. User system 305 may be an application implemented on a mobilephone, an internet browser located on a computer with access to theinternet, or any other type of implementation with capability to providecommunications between user 300 and QA system 320 across any type ofnetwork 310. User system 305 may include multiple computing devices incommunication with each other and with other devices or components viaone or more wired and/or wireless data communication links, where eachcommunication link may comprise one or more of wires, routers, switches,transmitters, receivers, or the like. User system 305 may include acomputer with a displayed menu or other readable program suitable forthe user to type in a question and to provide to the user any answer ina displayed manner. User system 305 may also be a mobile phone with atype of voice recognition system to allow the user to generate an oralinquiry and to provide any answer through a speaker. Alternativeembodiments may provide many other types of devices and methods to allowa user to generate a query and to display or otherwise provide anyanswer to the user.

QA (question/query and answer) system 320 includes a natural languageprocessing (NLP) based user interface 325, a QA manager 330, userdatabase 332, analytical models 335 and a set of databases 340 (whichmay include user database 332). User interface 325 provides an interfacefrom QA manager 330 and user system 305 and can perform a variety offunctions including identifying users, utilizing NLP, andformatting/redacting information, all under the management of QA system320. Some of the functionality of user interface 325 may be provided byuser system 305 in alternative embodiments. QA system 320 may include acomputing device (comprising one or more processors and one or morememories, and potentially any other computing device elements generallyknown in the art including buses, storage devices, communicationinterfaces, and the like) connected to the network 310. QA system 320and network 310 may enable question/answer (QA) generation functionalityfor one or more content users. Other embodiments of QA system 320 may beused with components, systems, sub-systems, and/or devices other thanthose that are depicted herein.

QA system 320 may be configured to receive inputs from various sources.For example, QA system 320 may receive input from network 310, a corporaof electronic documents 350, a content creator, content users, and otherpossible sources of input. In one embodiment, some or all of the inputsto QA system 320 may be routed through network 310. The variouscomputing devices on the network may include access points for contentcreators and content users. Some of the computing devices may includedevices for a database storing the corpus of data. The network mayinclude local network connections and remote connections in variousembodiments, such that QA system may operate in environments of anysize, including local and global, e.g., the Internet. Additionally, QAsystem 320 serves as a front-end system that can make available avariety of knowledge extracted from or represented in documents,network-accessible sources and/or structured data sources. In thismanner, some processes populate the knowledge manager with the knowledgemanager also including input interfaces to receive knowledge requestsand respond accordingly.

A content creator creates content in a document or other corpus for useas part of corpora with QA system. The document may include any file,text, article, or source of data for use in QA system 320. Content usersmay access QA system 320 via a network connection or an Internetconnection to network 310, and may input questions to QA system 320 thatmay be answered by the content in the corpus of data. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query it from the QA system.One convention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using natural language processing(NLP). In one embodiment, the process sends well-formed questions (e.g.,natural language questions, etc.) to the knowledge manager. QA system320 may interpret the question and provide a response to the contentuser containing one or more answers to the question. In someembodiments, QA system 320 may provide a response to users in a rankedlist of answers.

QA system 320 may be the IBM Watson™ QA system available fromInternational Business Machines Corporation of Armonk, N.Y., which isaugmented with the mechanisms of the illustrative embodiments describedherein. The knowledge manager system may receive an input question whichit then parses to extract the major features of the question, that inturn are then used to formulate queries that are applied to the corpusof data. Based on the application of the queries to the corpus of data,a set of hypotheses, or candidate answers to the input question, aregenerated by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA system. The statisticalmodel may then be used to summarize a level of confidence that the QAsystem has regarding the evidence that the potential response, i.e.candidate answer, is inferred by the question. This process may berepeated for each of the candidate answers until the QA systemidentifies candidate answers that surface as being significantlystronger than others and thus, generates a final answer, or ranked setof answers, for the input question. More information about the IBMWatson™ QA system may be obtained, for example, from the IBM Corporationwebsite, IBM Redbooks, and the like.

User interface 325 can handle several functions under the direction ofQA manager 330. First, user interface 325 can be utilized to determinethe identity of a user or to identify a class of user that the userbelongs to under the instruction of QA manager 330 and in concert withuser database 332. This will then be utilized to identify thecredentials of the user. Credentials are the verified access privilegeor security clearance (i.e., access rights) that a user has to view orotherwise access restricted data and data sources. These credentials maybe based on various criteria such as whether the user is employed by acertain entity (e.g., governmental or private company), is employed at acertain level within that entity, has a verifiable security clearance,has paid a fee for access credentials such as with certain informationbrokers and sellers, etc. These credentials can be utilized to determinewhether an answer should be redacted such as described below. Forexample, if the user is not identifiable or is identified as not havingappropriate credentials to access sensitive material, then the answersare redacted to hide any sensitive information. However, enoughinformation may be allowed to indicate to the user that better answersmay be available with the appropriate credentials.

Second, user interface 325 can process questions from users using NLP todecompose, classify, transform, and otherwise analyze to put thosequestions in a form easily usable by QA manager 330. For example, userinterface 325 can determine from a user system communication that aquestion has been asked, the class of question (i.e., whether thequestion is asking for a date, a name, a place, etc.) as well as providethe question in a standard format for processing by QA manager 330.Third, user interface 325 can then take answers received from QA manager330 and again utilize NLP to provide those answers in a humanunderstandable or appreciated form for transmission back to the user.This process can include formatting the answers appropriate for the userincluding any redactions imposed by redaction filter 331 based on thepreviously identified credentials of the user. The redacted and NLPformatted answer can then be provided to user system 305 across network310 for final communication to the user.

QA manager 330 can take an NLP (natural language processing) decomposed,classified, transformed and otherwise analyzed question and utilizeanalytical models 335 and databases 340 to determine a set of answerswith a corresponding set of confidence in those answers. These answerscan be modified by redaction filter 331 based on user credentialsdescribed in user database 332 as described below. Those answers canthen be communicated to user interface 325 for formatting andtransmission to the user. Although redaction filter 331 is illustratedas a sub-module of QA manager 330, it may be tightly incorporated intoQA manager 330. Alternatively, redaction filter 331 may be a separatemodule between and in direct communication with user database 332 anduser interface 325. In any case, redaction filter may also have directcommunications with user database 332 and user interface 325 asindicated by the dashed arrows. Analytical models 335 include a set ofprogrammed processes or algorithms that can be utilized to generate aset of answers utilizing databases 340. Similar to weather forecastingsuch as projections of hurricane paths, different models are based ondifferent assumptions, utilize different methods of analysis and providedifferent results. However, no one model is best in all circumstances.Therefore, the results of the different models will be weighted andcombined to provide the best set of answers to a given question. Thisweighting can vary depending on identified circumstances.

Databases 340 include a set or source documents also referred to hereinas source documents (corpora) 350, NLP derived data 360, model weights370, statistical information 380 and historical data 390. Corpora 350includes documents and other information or content which can originatefrom a variety of sources including the internet, on-line and hardcopypublications, private documents, etc. which are in or converted toelectronic form. Corpora 350 include public corpora 352 and privatecorpora 354. Public corpora 352 are webpages, documents, recordings,pictures, or other types of information or content that are publiclyavailable or otherwise not sensitive (such as internal documents thatare not sensitive for those accessing the information). That is, thereare no concerns about sharing the contents of public or othernon-sensitive corpora 352 with any user. Private or sensitive corpora354 are also webpages, documents, recordings, pictures, or other typesof information or content that are not readily publicly available or areotherwise sensitive. This can include information or content that may beconfidential or secret due to corporate, governmental, or other securityrestrictions. This can also include documents or other content orinformation that is private due to privacy concerns. Other types ofcorpora can be considered public (non-sensitive) or private for avariety of other reasons. That is, the corpora may be all internal, allexternal, all public, all private or a mixture thereof depending on theneeds of the users.

NLP derived data 360 includes search indices 362 and other data 364which is derived from corpora 350. That is, corpora 350 are preprocessedto extract search indices 362 and other data 364 for rapid access andanalysis by QA manager 330. Search indices 362 can be by word, byphrase, or by other format to capture and index the content of eachcorpus (document, information or content) within corpora 350. Forexample, words within a certain distance of each other within a corpusmay be readily identified by utilizing the search indices such as theword “penny” within 3 words of “wheatback” to identify a corpusdiscussing pennies minted between 1909 and 1959. Search indices caninclude certain relevant data for a given corpus such as when and whereit was published, whether the underlying corpus is public or private,etc. Other data 364 can include other information relating to thecorpora such as classifications of documents to describe whether a givendocuments (e.g. magazine, webpage) is fact or fiction based, reliabilityof that document, etc.

Model weights 370 includes weights applied to the various analyticalmodels 335 which may be utilized for determining the probability and/orconfidence that certain answers are correct. For example, many modelsmay be utilized to provide an answer with three models weighted 30% andanother model weighted 10%. The results are then derived from a weightedaverage or other combination of each model's results. These weights mayvary based on various factors such as the type of question, the subjectarea, etc. These weights can be determined and adjusted through varioustests demonstrating which models work best in different situations. Thiscan include analyzing an underlying set of test data used to develop themodels as well as analyzing actual questions and responses as the modelsare utilized in an implementation. Such test data as well as actualquestions and answers may be stored as historical data 390. Statisticalinformation 380 can include information generated while analyzinghistorical data 390 to generated model weights 370. Additional types ofstatistical information may be gathered over time such as demographicsof users. By storing such statistical information, future statisticalanalysis can build upon prior analysis so that the QA system can beimproved over time. These improvements can include improvements to modelweights 370 as well as possible improvements to the underlyinganalytical models 335. To help prevent the inadvertent leakage ofprivate information, historical data may be segregated into public data392 and private data 394.

FIGS. 4A to 4F are pictorial diagrams of a user interface in whichvarious embodiments may be implemented. These diagrams may be of awebpage, an application interface, or other interface which the user canaccess through a user system such as described above. Each diagramillustrates a user interface page which may be part of a window or otherlarger displayed element. In these diagrams, the pages are shown inEnglish, although other languages may be utilized.

FIG. 4A illustrates an opening page 400 where a user can ask a questionin question area 405 using natural language. A question has been enteredby a user in question area 405. This question is seeking a monetarynumber as can be determined utilizing natural language processing.Question area 405 may expand as the user types the question to includelonger questions. FIG. 4B illustrates an answer page 410 where thepreviously entered question is presented in question area 415 and theanswer is provided in answer area 420. In this example, there are threeanswers shown in field 422, each with a confidence 424 and aninformation button 426. The answers and confidence can be determinedutilizing the QA system described in FIG. 3 above or through alternativesystems. Each answer 422 is shown in this example because the user isdeemed to have the appropriate credentials or the answers are deemed tobe not sensitive. The confidence 424 is a weighting or statisticalconfidence in each answer utilizing techniques known to those ofordinary skill in the art. Typically, the confidences will add up toless than 100%. Information buttons 426 may be pressed or clicked by theuser to view the underlying source document(s) that provide the answer.For example, the first answer with the highest confidence may be from acredit reporting agency, the second answer from a trade publication, andthe third answer may come from public statements by a representative ofXYZ Corporation in a local newspaper. If the user does not have thecredentials to view certain sensitive source documents, then anyreferences to those sensitive source documents will be redacted so theuser cannot access them. Each source document may have information aboutits reliability as described above with reference to other data 364.

FIG. 4C illustrates an answer page 430 where certain information may beredacted due to the sensitivity of that information and the lack ofcredentials of the user. The redaction in this case is performed byblacking out the sensitive information. The previously entered questionis presented in question area 435 and the answer is provided in answerarea 440. In this example, there are two answers and one redacted answer448 shown in field 442, each with a confidence 444 and an informationbutton 446. The information button for redacted answer is also redactedas the underlying source documents cannot be revealed to the user.Alternatively, the information button for answer 448 may not be redactedto allow the user to request the information redacted and be allowed topresent his or her credentials. The redacted information button may alsobe kept active for the user to press or click to request the redactedinformation. However, if the user does not have the credentials to viewcertain sensitive source documents, then any references to thosesensitive source documents will be redacted so the user cannot accessthem. The answers and confidence in each answer can be determinedutilizing the QA system such as described above. As illustrated in thisfigure, the user is allowed to see that there is a better answer with ahigh confidence. In some embodiments, the set of answers may include asingle best answer. In such embodiments, the second best answer may bedisplayed with an indication that the best answer is hidden andavailable with further authentication or a fee.

In some alternative embodiments, the source information may be displayedalong with the answers to provide support. However, if some sourceinformation is considered sensitive, then that sensitive source materialshould be redacted, even if the resulting answer may not be sensitive orredacted. The answers and any supporting source material (evidence) arecollectively referred to herein as answer information including a set ofanswer information items. It is the sensitive portions of that answerinformation which is redacted, whether it is the answer or theunderlying source material.

FIG. 4D illustrates an answer page 450 where certain information may beredacted due to the sensitivity of that information and the lack ofcredentials of the user. The redaction in this case is performed byremoving the sensitive information. The previously entered question ispresented in question area 455 and the answer is provided in answer area460. In this example, there are two answers shown in field 462, eachwith a confidence 464 and an information button 466. Even if an answeris not sensitive, there may be sensitive source documents used withpublic or other non-sensitive documents to generate that non-sensitiveanswer. If the user presses the information button and if the user doesnot have the credentials to view certain sensitive source documents,then any references to those sensitive source documents will be redactedso the user cannot access them. As illustrated in this figure, the useris not allowed to see that there is a better answer with a highconfidence, although the user can see that the answers provided have alow confidence.

FIG. 4E illustrates a pop up box 470 or other element where a user cansubmit 478 a user name or other identifier 474 and password 476 toobtain the requested answer 472. The user can press or click on aredacted answer or information button such as shown in FIG. 4C above toinitiate pop up box 470. Alternatively, the user may right click theanswer area or use alternatively techniques to initiate pop up box 470.In another alternative, the user may provide the requested informationprior to asking a question. After entering user name 474 and password476 and then pressing or clicking submit button 478, the QA system cancheck the user database to determine if the user has the necessarycredentials to view the sensitive information that has been redacted. Ifyes, then the information is displayed such as shown above withreference to FIG. 4C. Otherwise, the information is not provided.

FIG. 4F illustrates a pop up box 480 or other element where a user caninitiate payment 488 as described in instruction field 484 to obtain therequested answer 482. The user can press or click on a redacted answeror information button such as shown in FIG. 4C above to initiate pop upbox 480. Alternatively, the user may right click the answer area or usealternatively techniques to initiate pop up box 480. Instruction field484 explains the process needed to obtain the answer. This descriptioncan include the price for that information. The user then presses orclicks on button 488 to go through a process for purchasing therequested information.

FIG. 5 is a flow diagram of generating and redacting answers inaccordance with a first embodiment. In a first step 500, a question andanswer session is initiated with a user by the user interface. This canbe accomplished by the user opening an application within a user device,by the user opening a webpage with an internet browser, or other methodsof initiating a session. This initiation process can includeestablishing a secure connection with the user including receiving andverifying the credentials of the user. In a second step 505, a questionpage such as shown in FIG. 4A above is provided to the user device anddisplayed to the user. In a third step 510, a question is received fromthe user through the user device to the user interface.

In a subsequent step 515, the user question is decomposed, classified,transformed and otherwise analyzed by the user interface utilizingnatural language processing to an easily managed query. Then in step520, the QA system takes the query and generates a set of answers, eachanswer having a percentage or other measure of confidence as well asidentifiers for a set of sources for that answer. The source identifiersare a provenance for each source and can include identifiers for thecorpus, the document, the page, the passage, etc. of each sourceutilized to generate each answer. The answer and any supporting sourcematerial including source identifiers are collectively referred toherein as answer information including a set of answer informationitems. It is the sensitive portions of that answer information which isredacted, whether it is the answer or the underlying source material. Inaddition, any graphical user interface to access sensitive answerinformation may be similarly redacted depending on the circumstances.This process is described below with reference to FIGS. 7A and 7B. In asubsequent step 525, the redaction filter then reviews the sources ofeach answer. Then in step 530, the redaction filter determines whetherany of the answers contain sensitive information. If yes, the processingcontinues to step 535, otherwise processing proceeds to step 555 below.

In step 535, it is determined whether the user has already provided hisor her credentials. If yes, then processing continues to step 545. Ifnot, then in step 540, the credentials are obtained from the user andprocessing continues to step 545. The credentials may be a useridentifier and password, a set of secure information providedautomatically by the user system, a payment made by the user for access,etc. In step 545, the user credentials are compared to the sensitiveinformation to determine whether the user has permission to access thesensitive information. If the user has permission, then processingcontinues to step 555. If the user does not have permission, then instep 550 the answer is redacted such as shown in FIGS. 4C and 4D above.The type of redaction is in accordance with the implementation of the QAsystem. The type of redaction can be based on the credentials or thelack thereof from the user. For example, if a user does not provide anyidentifiable or verifiable credentials, then the redacted answer mayappear as shown in FIG. 4D above. If the user does provide verifiablecredentials, but the credentials are insufficient to provide an answerto the user, then the redacted answer may appears as shown in FIG. 4Cabove. Alternative embodiments may utilize alternative redactionschemes. Processing then continues to step 555 where the answersprovided by the QA system, redacted or not, are provided to the userthrough the user interface and user system. When the answers arepresented to the user, the user may be able to request the underlyingsource documents utilized for generating that answer. Those sourcedocuments or references (e.g., web links) to those source documents maythen be provided to the user. However, if the user does not have thecredentials to view certain sensitive source documents, then anyreferences to those sensitive source documents will be redacted so theuser cannot access them. Processing then returns to step 505 above.

FIG. 6 is a flow diagram of generating and redacting answers inaccordance with a second embodiment. In this embodiment, there are twolevels of sensitive information and user credentials which are obtainedup front. This embodiment can provide greater protection of sensitiveinformation in many circumstances. In a first step 600, a question andanswer session is initiated with a user by the user interface. This canbe accomplished by the user opening an application within a user device,by the user opening a webpage with an internet browser, or other methodsof initiating a session.

In a second step 605, the user logs into the system using a userid andpassword or other types of authentication such as using biometrics. Theuser may even be a software program or a software agent for a user. Insuch as case, the authentication may be through the use of secureencryption keys. Once the user logs into the system, then in step 610,the system obtains the credentials of the user from the user database.These credentials may have been previously obtained and verified foreach user to build the user database. The user database may have beenestablished using a variety of techniques such as by querying anemployee database within a company or other entity. For example, if theuser is a member of the human relations department, that user may begranted access to a large variety of sensitive information compared toan engineer who may be granted access to sensitive information specificto a certain technology. For another example, a person working for onegovernmental agency may be granted access to sensitive informationwithin that agency, but not other agencies. The user database may havebeen established with users paying an annual fee. For example, the usermay be able to obtain different levels of access of sensitiveinformation depending on how much the user pays for a subscription. Thatis, there may be several subscription levels depending on the type ofsensitive information that the user is willing to purchase. Forillustrative purposes, this embodiment is limited to two levels ofsensitivity. However, many levels and types of sensitivity may beimplemented, and those levels or types may not be hierarchical. Forexample, three different users may all be able to access different typesof sensitive information not accessible by the other users.

Then in step 615 a question page such as shown in FIG. 4A above isprovided to the user device and displayed to the user. In a subsequentstep 620, a question is received from the user through the user deviceto the user interface. In step 625, the user question is decomposed,classified, transformed and otherwise analyzed by the user interfaceutilizing natural language processing to an easily managed query.

Then in step 630, the QA system takes the query and generates a set ofanswers, each answer having a percentage or other measure of confidenceas well as identifiers for a set of sources for that answer. The sourceidentifiers are a provenance for each source and can include identifiersfor the corpus, the document, the page, the passage, etc. of each sourceutilized to generate each answer. This process is described below withreference to FIGS. 7A and 7B. In a subsequent step 635, the redactionfilter then reviews the sources of each answer. In step 640, theredaction filter determines whether any of the answers contain sensitiveinformation. If yes, the processing continues to step 645, otherwiseprocessing proceeds to step 665 below.

In step 645, the corpora sources for one of the answers are checked tosee whether the user has the credentials for accessing all of thosesources. If not, then processing continues to step 650 to determinewhether that answer is available from corpora sources other than sourcesthat the user does not have the credentials to access. If no in step650, then in step 655 the answer is redacted and processing continues tostep 660. The type of redaction is in accordance with the implementationof the QA system. The type of redaction can be based on the credentialsor the lack thereof from the user. For example, the redaction may be inthe form of FIG. 4C where the user can see that an answer was redacted.However, if the source of information is particularly sensitive and theuser has a low level of credentials to view sensitive information, thenthe redaction may be in the form of FIG. 4D where the user cannot seethat an answer was redacted. Alternative embodiments may utilizealternative redaction schemes. If yes in steps 645 or 650, then theanswer is not redacted and processing continues to step 660. In step660, it is determined whether there are any additional answers to bereviewed for sensitive sources. If yes, then processing returns to step645, otherwise processing continues to step 665. In step 665 the QAsystem provides the answers, redacted or not, to the user through theuser interface and user system. When the answers are presented to theuser, the user may be able to request the underlying source documentsutilized for generating that answer. Those source documents orreferences (e.g., web links) to those source documents may then beprovided to the user. However, if the user does not have the credentialsto view certain sensitive source documents, then any references to thosesensitive source documents will be redacted so the user cannot accessthem. Then in step 670, it is determined whether the user seeksadditional credentials, such as when certain answers have been redacted.If not, then processing returns to step 615 for the next user question,otherwise the user credentials are obtained in step 675. This caninvolve the user providing additional, information indicating his or herright to access certain information. It can also involve the user payingfor those credentials. Once received, then processing returns to step640 to reprocess the answers based on the new credentials.

FIGS. 7A and 7B are flow diagrams of generating a set of answers forpossible redaction in which various embodiments may be implemented. FIG.7A is a flow diagram of collecting and analyzing a set of sourcedocuments which then can be utilized for generating answers. In a firststep 700, a document is received for addition to the corpora. In step705, a record of the document is created in the corpora databaseincluding an identifier of the document and any sensitivity of thedocument. In step 710, the document is parsed, indexed, classified andcatalogued utilizing natural language processing (NLP). This includesidentifying the type of document (such as whether it originates from anencyclopedia or a webpage), reliability of the document, sensitivity ofthe document, as well as indices of the document such as identifying therelative location of keywords within the document. This information isthen stored in into an NLP derived database with the document identifierin step 715. Additional statistical information may be collected orotherwise accumulated from the document such as size, age, etc. andstored in a statistical information database in step 720. Thisstatistical information may be useful later for performing statisticalanalysis of the corpora and possibly for improving the analyticalmodels. Processing then returns to step 700 for processing the nextdocument.

FIG. 7B is a flow diagram of generating a set of answers for possibleredaction. In a first step 750, an NLP analyzed query is receivedincluding classification and other analytical data from the userinterface. Then in step 755, the analytical models are provided thequery and analytical data. In step 760, each model utilizes the searchindices and other data to identify a set of public corpora and privatecorpora, and their source identifiers, for use in identifying answers tothe query. The source identifiers are a provenance for each corporasource and can include identifiers for the corpus, the document, thepage, the passage, etc. of each source utilized to generate each answer.In step 765, each analytical model utilizes the identified public andprivate corpora to generate a set of answers, each answer having apercentage or other measure of confidence as well as the identifiers ofthe corpora sources for that answer. This can involve identifyingfactual information from the public and private corpora based on thesearch indices and other data. In many cases, the search indices alonemay be sufficient to provide the answers without necessarily accessingthe source corpora.

Then in step 770, the sets of answers from each model are combinedutilizing a set of model weights depending on the classification andother analytical data from the user interface. This combinationtypically includes multiple answers, each with a weighted measure ofconfidence. For example, answers derived from works of fiction wouldhave a much lower measure of confidence than answers derived fromrespected reference sources. In step 775, the weighted measures ofconfidence are then normalized for each answer. In step 780, it isdetermined whether there are an excessive number of answers with a verylow weighting. For example, if there are 20 answers with 15 of thoseanswers having a normalized measure of confidence less than 1%, thenthose 15 answers may be removed and not utilized. This is not redactionfor sensitivity reasons, but for avoiding providing an excessive numberof answers with a low probability of being correct. If yes in step 780,then those answers are removed in step 785. Processing then continues tostep 790. In step 790, the resulting answers with their weighted measureof confidence and identifiers of corpora sources are then passed to theredaction filter for possible redaction. Processing then ceases untilthe next query is received.

FIGS. 8A to 8E are block diagrams of types of database records in whichvarious embodiments may be implemented. A record is a set of informationwithin a domain or database that establishes a relationship between aset of data or data elements. A record may be a separate entry into adatabase, a set of links between data, or other logical relationshipbetween a set of data. FIG. 8A is a block diagram of a record 800 storedin a source document (corpora) database. FIG. 8B is a block diagram of arecord 820 stored in an NLP derived database which is utilized toquickly access corpora records. FIG. 8C is a block diagram of a record840 stored in a model weights database utilized to weigh results fromanalytical models. FIG. 8D is a block diagram of a record 860 stored inhistorical database 390 used to retain historical data for futurestatistical analysis. FIG. 8E is a block diagram of a record 880 storedin a user database. The records described below are examples andalternative embodiments may utilize other structures and types of datautilized for implementation.

FIG. 8A is a block diagram of a record 800 stored in a source document(corpora) database. As described above, a source document, informationor content can be a human or machine generated publication, webpage,picture, sound or other document or content which may be sensitive orpublicly available. There may be one record for each document, group ofdocuments, or portion of a document. Each record includes a documentidentifier (ID) 802, sensitivity information 804, and the documentitself 806. The document identifier is a unique number or otheridentifier for quickly identifying and accessing the document.Sensitivity information 804 includes information about whether thedocument is sensitive and what credentials are needed to access thatdocument. Document 806 includes the actual text, picture, sound, etc.that forms the document.

FIG. 8B is a block diagram of a record 820 stored in an NLP deriveddatabase which is utilized to quickly access corpora records. There maybe one record for each record in the corpora database, although multiplerecords may be utilized. Record 820 include a document identifier 822 tocross reference with the corpora database, sensitivity information 824,type of document 826 (such as whether it originates from an encyclopediaor a webpage), reliability of the document 828, as well as indices ofthe document 830 such as identifying the relative location of keywordswithin the document. This information can then be utilized to quicklyidentify relevant documents for answering any questions.

FIG. 8C is a block diagram of a record 840 stored in a model weightsdatabase utilized to weigh results from analytical models. This includesone record for each set of weights that may be utilized. That is, aquestion with a certain type of question, the subject areaclassification, user profile, etc. may have a different set of weightsapplied based on statistical analysis of training and actual sampledata. Record 840 includes question type 842, question classification844, user profile 846, other characteristics 848 and analytic modelweights 850. Weights 850 can include a weighting factor for every modelthat may be utilized.

FIG. 8D is a block diagram of a record 860 stored in historical database390 used to retain historical data for future statistical analysis. Thisis to capture actual data or samples of actual data for future analysisand use. Record 860 includes an NLP analyzed question 862, the proposedanswers 864 prior to redaction (which may require that certain data besegregated for protecting that information), the sensitivity 866 of eachanswer, the measure of confidence 868 of each answer, and other data 870that may be useful for storage including the user profile of the personthat asked the question.

FIG. 8E is a block diagram of a record 880 stored in a user database.This is to allow a user or an entity to provide his or her credentialsfor immediate or future use. Record 880 includes a user identifier 882,password 884 to allow the user to log in as desired. Record 880 alsoincludes a user profile 886 for use in providing greater improvement ofanalytical model weighting. Further included are user credentials 888for allowing that user to access certain sensitive information.

The invention can take the form of an entirely software embodiment, oran embodiment containing both hardware and software elements. In apreferred embodiment, the embodiments are implemented in software orprogram code, which includes but is not limited to firmware, residentsoftware, and microcode.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), or Flash memory, an opticalfiber, a portable compact disc read-only memory (CD-ROM), an opticalstorage device, a magnetic storage device, or any suitable combinationof the foregoing. In the context of this document, a computer readablestorage medium may be any tangible medium that can contain, or store aprogram for use by or in connection with an instruction executionsystem, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electromagnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing. Further, a computer storage medium may contain or store acomputer-readable program code such that when the computer-readableprogram code is executed on a computer, the execution of thiscomputer-readable program code causes the computer to transmit anothercomputer-readable program code over a communications link. Thiscommunications link may use a medium that is, for example withoutlimitation, physical or wireless.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage media, and cache memories, which provide temporary storage of atleast some program code in order to reduce the number of times code mustbe retrieved from bulk storage media during execution.

A data processing system may act as a server data processing system or aclient data processing system. Server and client data processing systemsmay include data storage media that are computer usable, such as beingcomputer readable. A data storage medium associated with a server dataprocessing system may contain computer usable code such as for managinguser access to query results. A client data processing system maydownload that computer usable code, such as for storing on a datastorage medium associated with the client data processing system, or forusing in the client data processing system. The server data processingsystem may similarly upload computer usable code from the client dataprocessing system such as a content source. The computer usable coderesulting from a computer usable program product embodiment of theillustrative embodiments may be uploaded or downloaded using server andclient data processing systems in this manner.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to explain the principlesof the invention, the practical application, and to enable others ofordinary skill in the art to understand the invention for variousembodiments with various modifications as are suited to the particularuse contemplated.

The terminology used herein is for the purpose of describing particularembodiments and is not intended to be limiting of the invention. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1-9. (canceled)
 10. A computer usable program product comprising anon-transitory computer usable storage medium including computer usablecode for use in redacting QA system answer information based on useraccess to content, the computer usable program product comprising codefor performing the steps of: analyzing a corpus by natural languageprocessing techniques, wherein the corpora includes non-sensitive andsensitive content, and storing the analyzed corpora in memory; receivinga user question to be answered by utilizing the analyzed corpora;utilizing a processor to determine a set of answer information byprocessing using the corpora; determining a user access right tosensitive content; and redacting an answer information item from the setof answer information if sensitive content to which the user does nothave access was used to determine answer information item.
 11. Thecomputer usable program product of claim 10 wherein each of the set ofanswer information includes a corresponding measure of confidence. 12.The computer usable program product of claim 11 wherein redacting theanswer information item does not include redacting the correspondingmeasure of confidence.
 13. The computer usable program product of claim10 further comprising presenting the user with an option to submitcredentials to gain access to the redacted answer information item. 14.The computer usable program product of claim 10 further comprisingpresenting the user with an option to purchase access to the redactedanswer information item.
 15. A data processing system for redacting QAsystem answer information based on user access to content, the dataprocessing system comprising: a processor; and a memory storing programinstructions which when executed by the processor execute the steps of:analyzing a corpora by natural language processing techniques, whereinthe corpora includes non-sensitive and sensitive content, and storingthe analyzed corpora in memory; receiving a user question to be answeredby utilizing the analyzed corpora; utilizing the processor to determinea set of answer information by processing using the corpora; determininga user access right to sensitive content; and redacting an answerinformation item from the set of answer information if sensitive contentto which the user does not have access was used to determine answerinformation item.
 16. The data processing system of claim 15 whereineach of the set of answer information includes a corresponding measureof confidence.
 17. The data processing system if claim 16 whereinredacting the answer information item does not include redacting thecorresponding measure of confidence.
 18. The data processing system ofclaim 16 wherein redacting the answer information item includesredacting the corresponding measure of confidence while not affectingthe corresponding measure of confidence for answer information notredacted.
 19. The data processing system of claim 15 further comprisingpresenting the user with an option to submit credentials to gain accessto the redacted answer information item.
 20. The data processing systemof claim 15 further comprising presenting the user with an option topurchase access to the redacted answer information item.